Reducing Unnecessary Oophorectomies for Benign Ovarian Neoplasms in Pediatric Patients
Authors: Peter C. Minneci, Katherine C. Bergus, Carley Lutz, Jennifer Aldrink, Christina Bence, Lesley Breech, Patrick A. Dillon, Cynthia Downard, Peter F. Ehrlich, Mary Fallat, Jason D. Fraser, Julia Grabowski, Michael Helmrath, Paige Hertweck, Geri Hewitt, Ronald B. Hirschl, Rashmi Kabre, Dave R. Lal, Matthew Landman, Charles Leys, Grace Mak, Troy Markel, Joseph Pressey, Manish Raiji, Beth Rymeski, Jacqueline Saito, Thomas T. Sato, Shawn D. St Peter, Jennifer Cooper, Katherine Deans, Gail Besner, Jessica Kandel, Frederick Rescorla, Daniel von Allmen, Brad Warner, Thomas Inge, Steven Kasten, George Holcomb, David Gourlay, Petros Anagnostopoulos, Sarah Fox, Cory Criss, Elizabeth Speck, Samir Gadepalli, Kyle Van Arendonk, Katherine Flynn-O'Brien, Alan Ladd, Seth Goldstein, Mehul Raval, Tiffany Wright, Stewart Carter
Journal: JAMA (2023)

Importance Although most ovarian masses in children and adolescents are benign, many are managed with oophorectomy, which may be unnecessary and can have lifelong negative effects on health. Objective To evaluate the ability of a consensus-based preoperative risk stratification algorithm to discriminate between benign and malignant ovarian pathology and decrease unnecessary oophorectomies. Design, Setting, and Participants Pre/post interventional study of a risk stratification algorithm in patients aged 6 to 21 years undergoing surgery for an ovarian mass in an inpatient setting in 11 children’s hospitals in the United States between August 2018 and January 2021, with 1-year follow-up. Intervention Implementation of a consensus-based, preoperative risk stratification algorithm with 6 months of preintervention assessment, 6 months of intervention adoption, and 18 months of intervention. The intervention adoption cohort was excluded from statistical comparisons. Main Outcomes and Measures Unnecessary oophorectomies, defined as oophorectomy for a benign ovarian neoplasm based on final pathology or mass resolution. Results A total of 519 patients with a median age of 15.1 (IQR, 13.0-16.8) years were included in 3 phases: 96 in the preintervention phase (median age, 15.4 [IQR, 13.4-17.2] years; 11.5% non-Hispanic Black; 68.8% non-Hispanic White); 105 in the adoption phase; and 318 in the intervention phase (median age, 15.0 [IQR, 12.9-16.6)] years; 13.8% non-Hispanic Black; 53.5% non-Hispanic White). Benign disease was present in 93 (96.9%) in the preintervention cohort and 298 (93.7%) in the intervention cohort. The percentage of unnecessary oophorectomies decreased from 16.1% (15/93) preintervention to 8.4% (25/298) during the intervention (absolute reduction, 7.7% [95% CI, 0.4%-15.9%]; P = .03). Algorithm test performance for identifying benign lesions in the intervention cohort resulted in a sensitivity of 91.6% (95% CI, 88.5%-94.8%), a specificity of 90.0% (95% CI, 76.9%-100%), a positive predictive value of 99.3% (95% CI, 98.3%-100%), and a negative predictive value of 41.9% (95% CI, 27.1%-56.6%). The proportion of misclassification in the intervention phase (malignant disease treated with ovary-sparing surgery) was 0.7%. Algorithm adherence during the intervention phase was 95.0%, with fidelity of 81.8%. Conclusions and Relevance Unnecessary oophorectomies decreased with use of a preoperative risk stratification algorithm to identify lesions with a high likelihood of benign pathology that are appropriate for ovary-sparing surgery. Adoption of this algorithm might prevent unnecessary oophorectomy during adolescence and its lifelong consequences. Further studies are needed to determine barriers to algorithm adherence.